A delay-robust touristic plan recommendation using real-world public transportation information

Tourism Recommender Systems (TRS) assist tourists in designing a plan for a soon-to-be visited city, which consists of a selection of relevant points-of-interest (POI), the order in which they will be visited, the start and end time of the visits, etc. These tools filter POIs based on the tourist's preferences and take into account time constraints, like the desired duration of the plan, or the POI's opening or closing times. However, being able to provide tourists with an additional travel plan which explains how to reach those POIs using public transportation is a feature in which TRSs come short. Existing solutions try to solve the problem in a simplified way and do not model all possible events involved in using public transportation, such as combining transfer times and trips, changing vehicles, or dealing with delays of transportation units. We therefore propose three novel approaches to generate visit plans and their corresponding travel plans, namely SILS, TRILS and PHILS, which overcome these weaknesses. These approaches generate visit plans by iteratively adjusting them according to the traveling information and differ in the way the adjustment is done. Our experiments on a real-world POI dataset and public transportation information of the city of Izmir show that our approaches outperform the stateof-the-art in terms of quality of recommendations. Moreover, they are also able to provide both visit and travel plans in real-time and are robust in case of delays. To the best of our knowledge previous approaches have not been able to achieve this level of practicality.